A Bayesian spatio-temporal statistical analysis of Out-of-Hospital Cardiac Arrests
Tierney, Nicholas John
Caputo, Maria Luce
KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2020-02-03
Print Publication Date2020-07
Embargo End Date2021-01-20
Permanent link to this recordhttp://hdl.handle.net/10754/661091
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AbstractWe propose a Bayesian spatio-temporal statistical model for predicting Out-of-Hospital Cardiac Arrests (OHCA). Risk maps for Ticino, adjusted for demographic covariates, are built for explaining and forecasting the spatial distribution of OHCAs and their temporal dynamics. The occurrence intensity of the OHCA event in each area of interest, and the cardiac risk-based clustering of municipalities are efficiently estimated, through a statistical model that decomposes OHCA intensity into overall intensity, demographic fixed effects, spatially structured and unstructured random effects, time polynomial dependence and spatio-temporal random effect. In the studied geography, time evolution and dependence on demographic features are robust over different categories of OHCAs, but with variability in their spatial and spatio-temporal structure. Two main OHCA incidence-based clusters of municipalities are identified.
CitationPeluso, S., Mira, A., Rue, H., Tierney, N. J., Benvenuti, C., Cianella, R., … Auricchio, A. (2020). A Bayesian spatiotemporal statistical analysis of out-of-hospital cardiac arrests. Biometrical Journal, 62(4), 1105–1119. doi:10.1002/bimj.201900166
SponsorsFinancial support from Fondazione Fratelli Agostino Enrico Rocca is acknowledged. Antonietta Mira was partially supported by SNF grant 105218 166504. The authors thank the Swiss Cardiology Foundation.